LIDAR BASED FRACTURE ANALYSIS IN OUTCROPS OF CHATTANOOGA SHALE

ALONG THE WILLS VALLEY ANTICLINE, NORTHEASTERN ALABAMA

by

JEFFREY ALAN CLARK

IBRAHIM ÇEMEN, COMMITTEE CHAIR BERRY H. (NICK) TEW ERNEST A. MANCINI

YUEHAN LU

A THESIS

Submitted in partial fulfillment of the requirements for the degree of Master of Science

in the Department of Geological Sciences in the Graduate School of The University of Alabama

TUSCALOOSA, ALABAMA

2016

Copyright Jeffrey Clark, 2016 ALL RIGHTS RESERVED

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ABSTRACT

Unconventional reservoirs produce gas and/or oil from fracture porosity and permeability.

Therefore, understanding natural fracture patterns is important for effective production from

unconventional gas-shale reservoirs in that natural fractures influence reservoir behavior and

performance as they play vital roles in the movement of fluids such as oil and gas.

Understanding natural fractures within gas-shale reservoirs is also a critical factor in the

distribution of hydraulic fracture treatment design. The objective of this investigation is to

perform outcrop scale evaluation of fracture intensity by LIDAR (Light Detection and Ranging)

survey on organic rich Chattanooga Shale. Evaluation of fracture intensity is done by

determining the Fracture Spacing Index (FSI) of the shale at given stratigraphic intervals.

Organic rich Chattanooga Shale, a black shale formation, outcrops in several locations in

Northeastern Alabama. Two outcrops, of this study, are located along the backlimb of the Wills

Valley Anticline. During this investigation several close-up laser-scanned images of the shale

were collected across the outcrops using LIDAR. The outcrops are similar in their stratigraphy

and lithology; the base of the Chattanooga formation at both locations unconformably overlies

the Red Mountain formation. Its lower part is ductily deformed, while the upper units of the

shale at each outcrop become brittle and well-jointed with vertical fractures.

LIDAR provides highly accurate, representative data of natural fractures at the surface that can

improve subsurface models leading to more realistic reservoir characterization. Laser scanned

images at each outcrop provided highly accurate and precise digitized data which was analyzed

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to determine the relationship between bedding thickness and fracture spacing. The FSI,

determined from bed thickness/fracture spacing relationships, varies from 1.09 to 1.53 within

the brittle part of the Chattanooga Shale in both outcrops, implying a measure of

heterogeneity. Analysis of petrographic thin sections indicates that the brittle unit, with a high

index, is rich in quartz content with correspondingly larger grain sizes. This suggests that there

is a positive correlation between brittleness of a shale unit and its Fracture Spacing Index.

iv

DEDICATION

This work is dedicated to... ‘the shoulders of giants.’

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LIST OF ABBREVIATIONS AND SYMBOLS

AL-OGB State Oil and Gas Board

BRO Big Ridge Outcrop

Bt Bed Thickness term in Correlation Equation

BRO Big Ridge Outcrop

CSD Cross Strike Discontinuity

FPF Fault Propagation Fold

FPO Fort Payne Outcrop

FSI Fracture Spacing Index

FTB Fold Thrust Belt

K Proportionality Constant in Correlation Equation

LIDAR Light Detection and Ranging, Instrumentation

mya million years ago

Pf Pore Fluid Pressure

S Spacing term in Correlation Equation

SEM Scanning Electron Microscopy

TZ Transverse Zone

USGS United States Geological Survey

WVA Wills Valley Anticline

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ACKNOWLEDGMENTS

First and foremost, I would like to express my appreciation to Dr. Ibrahim Çemen for his

patience, support, and enthusiasm. His many conversations and willingness to share his

knowledge with me have been of immense value. I would also like to thank Dr. Yuehan Lu, Man

Lu, and Dr. Takehito Ikejiri for allowing me to further my understanding of Chattanooga Shale

from their research. I would like to thank wholeheartedly my committee members, Dr. Berry

(Nick) Tew and Dr. Ernest Mancini for their valuable advice and their patience during this study.

I would like to thank Dr. Harold Stowell and Dr. Delores Robinson for sharing their view of the

geological world, adding breadth to my understanding. Much appreciation is extended, as well,

to the Department of Geological Sciences at the University of Alabama for both allowing me to

work within the department, financially supporting aspects of the work, and assisting me in

presenting this material on regional and national levels. Thanks also to Dr. Kimberly Genareau

for allowing me to use her Scanning Electron Microscope.

Lastly, I would like to express my sincere thanks to Suleyman Alemdar for his friendship as we

journeyed together at the University of Alabama.

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CONTENTS

ABSTRACT ................................................................................................................................. ii

DEDICATION ............................................................................................................................ iv

LIST OF ABBREVIATIONS AND SYMBOLS .................................................................................. v

ACKNOWLEDGMENTS ............................................................................................................. vi

LIST OF FIGURES ...................................................................................................................... ix

CHAPTER I

INTRODUCTION ........................................................................................................................ 1

STATEMENT OF PURPOSE ........................................................................................................ 3

IMPORTANCE OF OUTCROP SCALE FRACTURE ANALYSIS........................................................ 4

RESEARCH QUESTIONS............................................................................................................. 8

METHODOLOGY ....................................................................................................................... 9

USE OF LIDAR IN OUTCROP SCALE FRACTURE ANALYSIS ...................................................... 11

CHAPTER II

GEOLOGIC OVERVIEW ............................................................................................................ 12

CHAPTER III

NATURAL FRACTURES ............................................................................................................ 18

IMPORTANCE OF NATURAL FRACTURES IN UNCONVENTIONAL RESERVOIRS ...................... 18

FORMATION OF NATURAL FRACTURES ................................................................................. 19

LIDAR IN OUTCROP SCALE FRACTURE ANALYSIS ................................................................... 21

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CHAPTER IV

FRACTURE ANALYSIS IN SELECTED CHATTANOOGA OUTCROPS ........................................... 22

STRATIGRAPHY OF THE CHATTANOOGA SHALE IN FPO AND BRO ........................................ 22

FORT PAYNE OUTCROP - FRACTURE ANALYSIS ..................................................................... 31

BIG RIDGE OUTCROP - FRACTURE ANALYSIS ......................................................................... 35

LIDAR APPLICATION RANGE LIMITS ....................................................................................... 36

CHAPTER V

CONCLUSIONS ........................................................................................................................ 39

CONCLUDING REMARKS ........................................................................................................ 42

REFERENCES ........................................................................................................................... 43

APPENDIX

(A) RIEGL LASER MEASUREMENT SYSTEMS ........................................................................... 56

(B) LIDAR IMAGING OF OUTCROPS – WORKFLOW ................................................................ 60

(C) LIDAR BASED FRACTURE STUDIES .................................................................................... 61

(D) FRACTURE DATA FROM LIDAR IMAGES AT CHATTANOOGA SHALE OUTCROPS ............. 64

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LIST OF FIGURES

Figure 1. Chattanooga Shale Fort Payne, Alabama ................................................................. 1

Figure 2. Location Map of Study Area ..................................................................................... 2

Figure 3. Traditional Inventory Square .................................................................................... 3

Figure 4. Fracture Density Measurements ............................................................................. 7

Figure 5. LIDAR Field Work ...................................................................................................... 9

Figure 6. LIDAR Scanning of Inventory Square, FPO .............................................................. 10

Figure 7. Foreland Basin Setting ............................................................................................ 13

Figure 8. Wills Valley Anticline Thrust Sheet ......................................................................... 14

Figure 9. Outcrops Locations on Geologic Map ..................................................................... 15

Figure 10 Mohr Circle Diagram for Stability Shifts ................................................................ 20

Figure 11. USGS Stratigraphic Nomenclature for Chattanooga Shale ................................... 23

Figure 12. Stratigraphic Correlations of Chattanooga Shale between outcrops ................... 24

Figure 13. Generalized Stratigraphy ...................................................................................... 25

Figure 14. Chattanooga Shale, Big Ridge LIDAR Images ........................................................ 26

Figure 15. Chattanooga Shale, Fort Payne LIDAR of Upper Inventory Square ...................... 27

Figure 16. Chattanooga Shale, Fort Payne LIDAR of Lower Inventory Squares..................... 28

Figure 17. LIDAR Images of Brittle Shale from Big Ridge and Fort Payne ............................. 29

Figure 18. Microscopy of Chattanooga Shale ....................................................................... 32

Figure 19. Fracture Density Plots Inventory Square 4, Fort Payne ........................................ 34

x

Figure 20. Fracture Density Plots Inventory Square 3, Fort Payne ........................................ 35

Figure 21. Fracture Density Plots, Big Ridge .......................................................................... 35

Figure 22. Range and Resolution ........................................................................................... 38

Figure 23. Quartz to Clay Ratios of Chattanooga Shale from Big Ridge and Fort Payne ....... 40

Figure 24. Data Evaluation from Inventory Square #4, FPO .................................................. 41

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CHAPTER I INTRODUCTION

The southern Appalachian fold-thrust belt in Alabama contains organic-rich shale units in

Cambrian and Devonian strata. One of these shale units is the Devonian Chattanooga Shale

(Figure 1). Outcrops of Chattanooga Shale in Northeastern Alabama contain well developed

natural fractures. During this study, two outcrops of

the Chattanooga Shale along the backlimb of the Wills

Valley Anticline in Northeastern Alabama (Figure 2)

were imaged with Terrestrial Laser Scanning,

commonly referred to as LIDAR (Light Detection and

Ranging). The natural fractures occurring in these

outcrops were evaluated in order to quantify

relationships between bedding thickness and fracture

spacing. Variations in fracture intensity, a measure of

relative density of fractures, was also evaluated with

respect to brittleness change vertically within each

outcrop.

Figure 1. Outcrop of Chattanooga Shale in Fort Payne, Alabama.

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Figure 2. Location Map of Study Area. Sedimentary Basins and Shale Plays across Alabama are indicated, including prospective shale plays within the Chattanooga Shale formation. The excerpt from the Geologic Map of Alabama has been modified and indicates outcrop locations.

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STATEMENT OF PURPOSE The main purpose of this investigation is to conduct outcrop scale evaluation of fracture

intensity and fracture density with respect to bedding thickness and brittleness variation in the

Chattanooga shale outcrops using images provided by a LIDAR survey. For this purpose, LIDAR

images of the two Chattanooga Shale outcrops along the Wills Valley Anticline, in Northeastern

Alabama, were obtained and studied in detail.

Previous studies on fracture analysis involved conventional field measurement techniques

using a measuring tape to measure representative and accessible areas along outcrops within

defined squares or circles. For example,

Ataman (2008) studied natural fractures

in Devonian Woodford shale outcrops in

the Arbuckle Mountains of Oklahoma.

He used traditional methodology in

which a limited area of accessible and

representative rock at an outcrop is

outlined and measured for bed thickness

and fracture frequency (Figure 3).

During this investigation, quantitative

evaluation of fracture density has been made possible by high precision LIDAR images. The

LIDAR images provided high precision measurements of fracture length, fracture spacing and

bedding thickness along with the ability to scan and quantitatively analyze larger and

inaccessible areas for greater representation. The results of LIDAR imaging demonstrate

Figure 3. Inventory Square from Woodford Shale used in traditional evaluation methods of bed thickness fracture density relationships. (Ataman, 2008)

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improvements in the measurement of the quantitative relationship between the fracture

spacing and bedding thickness as the measurements are not only more precise but also have

the benefit of facilitating the measurement of larger inventory squares which becomes

impractical by hand measurement.

Quantitative evaluation of natural fractures is important to enhance gas production in

unconventional shale-gas reservoirs as effective production models rely on accurate

representation of the density of fractures within the conduit which provides for fluid flow and

the reservoir permeability. Effectiveness of reservoir models can be improved by the input of

data from more accurate, higher resolution fracture relationships. Quantitative analysis of

natural fractures in two Chattanooga outcrops may be used for a better estimate of reservoir

permeability of potential Chattanooga shale unconventional reservoirs.

IMPORTANCE OF OUTCROP SCALE FRACTURE ANALYSIS

Stresses on the Earth’s crustal rocks create natural fractures as well as control their orientation.

Fracture frequencies and spatial distributions directly impact permeability and thus the fluid

flow in hydrocarbon reservoirs is, in part, controlled by fracture populations and their

connectivity (Gabrielsen, 1998). These natural fractures are often made up of genetically

different fractures sets which are commonly influenced by bed thickness, grain size,

compositional variation, fluid pressure, as well as influences from changes in local stress field

and temperature from burial and uplift which are associated with the tectonic regime. Variation

in production from shale gas units often can be tied to changes in the stress-dependent

fracture permeability (Bustin, et al., 2012; Ai, et al., 2014) which points toward the need to

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consider both natural and induced fractures as the reservoir stress changes impact conductivity

(Lorenz, 1999). Quantification of fracture relationships, such as fracture spacing and fracture

density, aids in both assessment of hydrocarbon flow (Rohrbaugh Jr., 2002) and potential

enhancement (Bustin, et al., 2012, Lorenz, 1999). The linear relationship between fracture

spacing and bed thickness (Ladeira and Price, 1981) which is tied to lithological variation is

reflected in the Equation below, usually referred to as the Correlation Equation.

S = K .Bt Correlation Equation

Where

S is the spacing between fractures,

Bt is the bed thickness and

K is the constant, strongly tied to differences in mechanical properties

from lithological/compositional differences in the rock unit.

Figure 4 shows a hypothetical example where bed thickness directly correlates to the spacing

between fractures, with generic measurements of bed thickness and fracture spacing. The

greater the bed thickness the greater the separation between fractures. The linearity and slope

of this relationship is dependent on compositional consistency and other factors which

contribute to heterogeneity within the rock, such as the degree and type of cementation

present. The data can be illustrated in a variety of ways including plots of bed thickness to

fracture spacing and plots of fracture density to bed thickness (Figure 4).

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This equation is a generic relationship which reflects the tendency for a fracture, once formed,

to impact subsequent fractures in a fashion where separation is regular, given a specific

lithology and consistent stress. Literature for several decades have pointed to the shortcomings

of the relationship resulting from competency contrast in adjacent beds (Rijken and Cooke,

2001; Ji and Saruwatari, 1998), the strain rate (Mandal, et al., 1994), fluid pressures, stress

shadows (Gross, et al., 1995), joint saturation (Tan, et al.,2014) and the impact of time in terms

of changes in the state of stress (Tuncay, Park, and Ortoleva, 2000; Olson, 2003) including those

derived from fluid pressures changing from thermal maturation of organic matter (Engelder,

2009). While the equation may not hold in all cases, the empirical relationship is valid (Ladiera

and Price, 1981) and can provide useful insight.

Analysis of bed thickness relative to fracture spacing is a measure of fracture density. This can

be measured across an outcrop and is useful in evaluating the adherence to the linear

relationship from the Correlation Equation. Descriptions of density relationships at given

stratigraphic intervals, at the surface, are often used in subsurface reservoir characterization.

Digital outcrop information can be extracted from LIDAR images. With the inherent high

resolution and precision available by laser scanning of outcrops, as well as the ability to

measure beds inaccessible by conventional field methods, the wealth of data within the images

can be statistically analyzed to extract the relationship between bedding thickness, fracture

spacing and fracture density precisely.

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Figure 4. Measurements of Fracture Density within Inventory Squares. A window (or square) of given dimensions is used to take inventory of the thickness of each bed as well as the spacing between fractures. The data can be represented in a variety of ways including the two above (Fracture Spacing to Bed Thickness and Fracture Density to Bed Thickness) as well as Bed Thickness to Number of Fractures or even more elaborate relationships such as Number of Fractures per Area of Plane or Length of Fractures per are of Plane. Data collection from inventory squares at varying stratigraphic levels within an outcrop provide the means to evaluate changes in populations of fractures.

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Modelling fracture networks in reservoirs by extrapolating parameters such as fracture density,

which has historically been derived from borehole image logs (Ma, et al., 1993; Kubik and

Lowry, 1993; Caramanica and Hill, 1994; Narr, 1996) or outcrop measurements, can improve

reservoir models by reducing uncertainty both in conventional and unconventional reservoirs

(Barthelemy, et al., 2008). Conventional workflow for constraining fracture information such as

fracture density into such models involves identifying fracture domains or networks within a

given area or sector followed by upscaling the data to populate the geological models (Boro, et

al., 2014). Fracture density data, which is rarely observable on cores, can effectively be obtained

from outcrop studies, especially with highly accurate and precise digital data produced by

LIDAR images (Gale, et al., 2014). Predicting zones for successful stimulation and production is

an essential goal (Glaser, et al., 2013) especially when seeking ‘Sweet Spots’ within

Unconventional Reservoirs.

Outcrop studies, including fracture density evaluation, are seeing a resurgence (Martinsen, et

al., 2011) as technological improvements in imaging provide high-resolution data can further

development of fracture network models with accurate representation being made on a meter

scale. LIDAR imaging generating highly accurate and precise digital records of outcrops is

providing much of the impetus behind the resurgence.

RESEARCH QUESTIONS

This investigation addresses the following research questions:

1) Can the application of LIDAR surveying be effective in the study of natural

fractures within outcrops of potential shale gas units such as the Chattanooga Shale?

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2) What relationship exists between fracture spacing and bed thickness within the

Chattanooga Shale?

3) How do two outcrops of Chattanooga Shale, separated by 40 kilometers (Figure 2),

along the backlimb of the Wills Valley Anticline vary in fracture intensity? Are controls

of fracture intensity variation evident?

4) What granular or compositional differences can be observed by microscopic study of

thin sections of the Chattanooga Shale within the outcrops? How do these variations

affect brittleness of the Chattanooga Shale and formation of natural fractures.

METHODOLOGY

During this study the RIEGL VZ-400 Laser

Scanning System (LIDAR) at the Big Ridge

Outcrop (BRO) and the Fort Payne Outcrop

(FPO) of Chattanooga Shale (Figure 2),

provided rich detailed images of scanned

areas which are evaluated for bed thickness

and fracture spacing (Figure 5). The images

facilitated fracture analysis as the digitization

of the spatial information leads to a data rich

point cloud which can be analyzed and

evaluated at a workstation with RIEGL’s

proprietary software, RiSCAN Pro.

Figure 5. LIDAR field work on Chattanooga Shale, FPO.

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The method used for analyzing fracture intensity within the outcrops of Chattanooga Shale

involved marking representative areas within each formation with a 1 x 1 meter square to

inventory fracture relationships (Figure 6). Once an inventory square was delineated on the

formation at stratigraphic intervals which had notable lithologic or mechanical changes, LIDAR

imaging of the target area on the outcrop surface commenced. Workup and Analysis involved

measurements of bed thickness and spacing between fractures within each inventory square

and representing the data through plots showing the relationship between bed thickness and

fracture spacing, such as those presented in Figure 4.

Figure 6. LIDAR scanning of the pre-marked inventory square leads to a digitized point cloud rich with spatially resolved information enabling fracture analysis with high measures of accuracy and precision. Chattanooga Shale, FPO.

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USE OF LIDAR IN OUTCROP SCALE FRACTURE ANALYSIS

LIDAR techniques operate by spatially resolving points across a surface referencing position and

distance relative to the imaging system. The functionality is tied to laser pulses sent from the

LIDAR system to a surface and reflected back, where travel time is the key factor in establishing

position and distance. With a terrestrial laser scanning system using a near infrared laser with

pinpoint accuracy detailed information of surficial features can be resolved. Appendix A is a

short summary of the RIEGL Laser Measurement System used during this study. Appendix B

provides the workflow of the LIDAR imaging of outcrops.

Imaging of outcrops by LIDAR provides opportunity for fractures within geologic bodies to be

measured and evaluated with precision. Fracture attributes characterizing clusters, intensity,

tracelengths, or orientations make outcrop analogues viable for improving reservoir models

(Watkins, et al., 2015). Fracture spacing, commonly represented by fracture density or

intensity, is known to correlate with bed thickness (Ladeira and Price, 1981; Wu and Pollard,

1995; Ji and Saruwatari, 1998; Schopfer, et al., 2011). Many recent studies have sought to

model the mechanics when the relationship holds in a rigorously linear fashion (Mandal, et al.,

1994; Gross, et al., 1995; Tan, et al., 2014) while some studies propose perspectives as to why a

linear relationship is, in principle, too generic (Narr and Lerche, 1984; Dershowitz and Herda,

1992; Narr, 1996; Rohrbaugh, et al., 2002). Ideally, the relationship holds true within a

homogeneous body (bed) of rock free from defects or flaws including cracks, irregular and

variable grain size, fractures, impurities, or fossils, which effectively reduce stress locally.

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CHAPTER II

GEOLOGIC OVERVIEW

The Appalachian foreland basin formed during a series of collisional orogenies over a span of

geologic time. The major orogenic events impacting the basin were the Taconic Orogeny (ca.

430 mya), the Acadian/Neo-Acadian Orogeny (ca. 360 mya), and the Alleghanian Orogeny (ca.

320 mya) (Hatcher, 2005). The tectonic dynamics throughout the multiple orogenic events

affected sedimentary patterns and drove the distribution of accumulated sediments

(Ettensohn, 2008).

The Devonian Chattanooga black shale was deposited in the retroarc foreland basin (Ver

Straeten, 2009) formed during the Acadian Orogeny (Figure 7). Chattanooga Shale is common

in the Southern Appalachian Fold-Thrust Belt (FTB) in Alabama in Northeast Alabama. The FTB

lies within the Valley and Ridge province and part of the Appalachian Plateau, spanning from

the Black Warrior Basin, the western boundary, to the Piedmont Metamorphic Belt to the east

at the Talladega Fault (Thomas, et al., 1984; Thomas and Neathery, 1980).

Within the elongate Northwestern domain of the FTB the Paleozoic rock units are deformed

into synclines and anticlines, including the Wills Valley Anticline area of study (Figure 8). The

Rising Fawn Cross Strike Discontinuity (CSD) or transverse zone can be found on the Northwest

end of the Wills Valley Anticline and the Anniston CSD is found on the Southeast end. Two

outcrops of the organic rich Devonian Chattanooga black shale are readily accessible along the

13

backlimb of the thrust sheet ramping from the sole detachment within the Rome-Conasauga

Shale with mid-level decollement within the Chattanooga (Coleman, et al., 1988).

Figure 7 A.) Idealized cross section of the retro-arc foreland basin during the Acadian. Black Shale deposition occurs cratonward from the orogeny (from Ver Straeten, 2009). B. Schematic of thrust belt orogenic elements which impact sediment deposition within the foreland. (from Johnson and Beaumont, 1995).

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Figure 8. Structural Variability within the Appalachian Thrust Belt.

A ) Simplified geologic map showing the location of the study area.

B) The structural suggesting that the Wills Valley anticline is the result of Trishear Fault Propagation Folding occurring from a thrust sheet splaying from a decollement surface in the Conasauga. The figures are modified from (A) Thomas and Bayona, 2005, and (B) Robinson, et al., 2012.

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Figure 9 indicates the location of the outcrops, of this study, in DeKalb and Etowah Counties.

The outcrops at Fort Payne (FPO) and Big Ridge (BRO) are indicated on the section of the

Geologic Map. A representation of the outcrops along the backlimb of the thrust sheet in the

Wills Valley Anticline is illustrated. Both outcrops have been detailed in guidebooks released

by the Alabama Geological Society (Coleman, et al., 1988; Thomas and Pashin, 2011) and

represent locally thick sections of Late Devonian Chattanooga Shale disconformably overlying

the sandstone of the Silurian Red Mountain Formation and capped by a thin bed of distinctly green

Maury Shale (Mississippian) followed by thick beds of Fort Payne Chert. The location furthest to the

Figure 9. Geologic Map of Study Area and Representation of Outcrop Locations along the backlimb of the Wills Valley Anticline. The Ft. Payne Outcrop (FPO) is located in Dekalb County while the Big Ridge Outcrop (BRO) is located to the southwest in Etowah County.

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north can be found near Fort Payne, Alabama, roughly 1.5 miles east of exit 218 on Interstate 59 ( N

34⁰ 28.229′ and W 85⁰ 42.193′) and, to the south, the locality designated as Big Ridge can be found

along Interstate 59 at mile marker 193 (N34⁰ 07.884 and W85⁰ 59.191). At these outcrops, the

Chattanooga ranges from 10 to 13 meters, and consists of black shale with well-developed natural

fractures within the upper, more brittle, section, whereas more ductile deformation characterizes

the lower section (Figure 1). The natural fractures within the upper section of the shale provide the

opportunity to evaluate fracture density using LIDAR imaging, as well as to draw comparisons

between the outcrops along strike.

The Chattanooga Shale is rich in organic material and often contains well developed natural

fracture networks in many outcrops. Conant and Swanson (1961) and Pashin, et al., (2011)

proposed that in the southern Appalachians, the Chattanooga Shale was deposited in a dysoxic

environment. Organic material within the shale is preserved with the limited presence of

oxygen, contributing to the organic richness of the Chattanooga Shale. With the onset of the

Alleghanian orogeny, the fold thrust belt of the foreland basin migrated northwestward with

compressional forces being mitigated by decollement, duplexing, thrusting, folding and

fracturing (Thomas and Neathery, 1980; Thomas, et al., 1984; Thomas and Neathery, 1982;

Pashin, et al, 2011). Much of structure within the Appalachian thrust belt in Alabama is the

result of thin-skinned thrusting, common within ductile shale, which facilitated decollement

surface development (Thomas and Bayona, 2002; Hatcher, 2004; Thomas, et al., 2007).

Robinson, et al., 2009 and Robinson, 2012, interpreted the Wills Valley Anticline as a Trishear

Fault Propagation Fold (FPF) with two subsequent breakthrough thrusts developing from

motion along the basal decollement in the Rome-Conasauga unit as it ramps upsection.

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Trishear FPF settings demonstrate decreased displacement along faults being accommodated

by deformation in shear zones radiating from tip lines (Hardy and Allmendinger, 2011). Within

the Rising Fawn CSD the McLemore Cove, Wills Valley, and Lookout Valley Anticlines all sole

within a lower decollement in the Conasauga Formation and are capped by roof thrust within

the Chattanooga Shale. These are thought to derive from the decollements in the Chattanooga

Shale propagating northwest in advance of the Conasauga decollement (Chowns, et al., 2004).

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CHAPTER III

NATURAL FRACTURES

Natural fractures within shale units are strongly linked to rock strength and their growth within

the rock occurs by mechanisms such as differential compaction, changes in tectonism, and

strain accommodation (Gale, et al., 2014). One current motivation for the study of natural

fractures is their role in gas and oil production in unconventional shale reservoirs. LIDAR

analysis of natural fractures in surface exposures of fine grained rocks offers the potential to

use this information to refine and enhance reservoir models and provide positive impacts on

production.

IMPORTANCE OF NATURAL FRACTURES IN UNCONVENTIONAL RESERVOIRS

Natural fractures within the shale play vital roles in the movement of fluids, such as oil, gas, and

water, through the rock and an understanding of these fractures is critical in designing and

implementing hydraulic fracture treatments of unconventional gas-shale reservoirs. Therefore,

understanding the natural fracture patterns within the shale is essential for unconventional gas-

shale reservoir production. Many early detailed fracture descriptions, crucial to proper

reservoir modelling, were made in the subsurface via borehole or core analysis (Kubik and

Lowry, 1993; Caramanica and Hill, 1994; Ma, et al, 1993; Narr and Lerche, 1984; Narr, 1996).

When possible, correlation between fractures in outcrops and those in the subsurface are of

19

great interest (Watkins, et al, 2015) in that key fracture attributes, such as spatial arrangements

and length, are only effectively measured in outcrops (Gale, et al., 2014). Mapping trends of

high fracture incidence or fracture density facilitates enhanced productivity (Narr and Lerche,

1984). With improvements in the ability to discern outcrop details from the highly accurate

LIDAR images, reservoir uncertainty can be reduced (Hodgetts, 2013; Seers and Hodgetts, 2014)

and production can be further improved.

FORMATION OF NATURAL FRACTURES

Lithologic characteristics from environments of deposition, as well as diagenesis and fluid

pressure during the thermal maturation play very important roles in the formation of natural

fractures within shale units. Devonian black shale units within the Appalachian Basin, including

the Chattanooga Shale, have natural fractures which were impacted by abnormal fluid

pressures generated during thermal maturation of the organic matter (Engelder, et al., 2009;

Lash and Engelder, 2009; Lash, et al., 2004). Figure 10 illustrates the change in fracture stability

associated with increasing pore pressure as the Mohr circle shifts from a stable applied normal

stress to an effective normal stress. If the pore pressure is sufficiently high, the shift within the

envelope of stability can lead to a critical point beyond which fractures can occur. Natural

fracture systems within shale beds can provide enhanced permeability or storage capacity and

improve the efficiency of gas production (Gale, et al., 2007). Production performance of shale

gas wells is heavily influenced by the fabric of the rock (Bustin and Bustin, 2012) in which the

fracture networks, heavily impacted by rock heterogeneity, play a large role. The fabric effectively

controls the permeability through the fractures as well as the microporous rock matrix. Gale, et

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al. (2014) point to the variability in fractures, and their growth, due to several possible

mechanisms (e.g. differential compaction, local and regional stress changes, strain

accommodation), as well as mechanical properties tied to the depositional, diagenetic, and

structural setting. Variability in fractures and jointing within a rock fabric is also connected to

structural deformation history, including potentially poly-phase deformation which effectively

reorients the local stress field (Engelder, 1987).

Figure 10. Mohr Circle Diagram illustrating the effect of Pore Fluid Pressure (Pf) on stability. The applied stress Mohr Circle well within the Mohr Stability Envelope is typical for stable rock in the earth having low differential stress (small diameter). Presence of fluids, such as hydrocarbons or injected water during hydraulic fracturing, effectively shift the circle to the left by reducing all applied normal stresses by the amount equivalent to the Pf The effective stress Mohr Circle, as illustrated is at the bounds of the stability envelope which defines the point of cohesive failure and fracturing. (Twiss and Moores, 2007)

21

LIDAR IN OUTCROP SCALE FRACTURE ANALYSIS

Within the last 10 years, there have been many studies using LIDAR images to study natural

fractures for outcrop scale reservoir characterization. Appendix C provides a short summary of the

major fracture analysis studies conducted in recent years using LIDAR images to better understand

natural fractures in the surface. These studies provided better reservoir permeability models for

reservoirs.

22

CHAPTER IV

FRACTURE ANALYSIS IN SELECTED CHATTANOOGA OUTCROPS

During this study, LIDAR images have been obtained from two Chattanooga Shale outcrops

along the backlimb of the Wills Valley Anticline in the Southern Appalachian FTB in order to

characterize the relationships of bedding thickness and fracture spacing in each outcrop along

the outcrop belt as well as stratigraphically at each locality. The FPO is located in Dekalb

County while the BRO is located to the southwest in Etowah County (Figure 9).

At the BRO the bedding within the well-jointed, brittle Chattanooga Shale strikes N44⁰E and

dips 18⁰ SE. One set of systematic fractures strike approximately N 70⁰ W. A cross-joint set of

fractures strike about N40⁰W. At the FPO the lower part of the formation strikes between

N20⁰E and N45⁰E and dips between 5 - 10⁰ SE. In the more brittle upper section, the beds strike

N10⁰W and dip 08⁰ SW. Some of the variability in orientation within the FPO might have

resulted from localized stress, not occurring on a regional scale and probably due to the ductile

deformation at the base of the formation which plays a role in frictional sliding during the

compressional tectonism and causes local structural thickening (Figure 8).

STRATIGRAPHY OF THE CHATTANOOGA SHALE IN FPO AND BRO

The Wills Valley Anticline formed as a Fault Propagation Fold (Robinson, et al., 2009) (Figure 8).

The natural fractures along the Wills Valley Anticline formed in response to stress mitigated by

lithologic variation. The BRO and FPO of Chattanooga Shale both exhibit a well-developed

23

system of fractures within the upper

brittle part of the section. The shale is

more ductile and does not fracture well

in the lower part of the section which is

associated with a decollement surface.

In Central Tennessee, the United States

Geological Survey recognizes distinct

members of the Chattanooga Shale

(Figure 11). The shale outcrops at the BRO and FPO in Northeastern Alabama contain the top of

the Dowelltown Member, Frasnian-Fammenian (Devonian) in age, and the Gassaway Member

(Lu, 2015). The upper, more brittle part, the Gassaway Member, is better suited for LIDAR based

fracture intensity studies because of its thick bedded brittle mechanical stratigraphy which gave

way to formation of well-developed natural fractures.

Correlations between bed thickness and frequency of fractures can provide stratigraphically-

defined relationships between bedding thickness and fracture density in the well fractured beds

of the Chattanooga Shale. Compositionally, the shale is predominantly mixtures of clay and

quartz (Lu, 2015) and the siliciclastic content increases upsection, with a notably distinct upper

section behaving in a more brittle fashion than the more ductily deformed lower section, as

seen at the FPO (Figure 1).

Lu (2015) divided the two outcrops into lithological subgroups with fissile shale at the base and

blocky shale at the top (Figure 12). The BRO contains distinct jointing in the upper 20 feet of the

in the formation (Pashin, et al., 2011). High resolution LIDAR images of predefined inventory

Figure 11. Stratigraphic Nomenclature adopted by the U.S.G.S from Chattanooga Shale in Central Tennessee (Glover, 1959). The blocky, well jointed Chattanooga Shale at the FPO and BRO is predominantly the Gassaway member.

24

squares have been acquired along the

outcrops at each location. Two inventory

square images at both FPO and BRO provide

fracture intensity data of the more brittle shale

which has been identified as the upper

Gassaway Member (Lu, 2015). While four

inventory squares were imaged at FPO, two

are from the more ductile section in the lower

part of the outcrop which is the low to middle

units of the Gassaway Member of the

Chattanooga Shale (Lu, 2015).

Figure 13 provides the relative stratigraphy for

each outcrop and also indicates the approximate Dowelltown-Gassaway boundary. The ductile

shale is towards the base of the formation while the brittle, well-jointed shale is found

upsection within the Gassaway member.

LIDAR imaging of outcrops provided precise data for analysis. High resolution, spatially resolved

images are made with laser precision leading to 3D data which lends itself to fracture intensity

analysis from each outcrop (Figure 14). During this study, LIDAR images were captured to

provide 3-D perspective to quantitatively study the relationships between fracture spacing and

bedding thickness. Images of one meter by one meter squares across the formation have been

Figure 12. Stratigraphic Correlation of Chattanooga Shale within the FPO (Outcrop 1) and the BRO (Outcrop 2). The purple line marks distinct shift in lithology. (Lu, 2015)

25

Figure 13. Relative generalized stratigraphy between each outcrop. The approximate boundary between the ductile Dowelltown and the brittle Gassaway member is indicated. Studies (Lu, 2015) show that Quartz to Clay ratios, determined by X Ray Diffraction, increase significantly from an average of 1.2 within the Dowelltown to an average of 3.4 within the Gassaway.

26

made to ‘inventory’ the intensity of fractures at relative stratigraphic levels. For example at

the BRO (Figure 14), the lower of the two inventory squares (1) along the highly deformed more

ductile base appears lithologically distinct from the upper inventory square (2) as the shale

appears to become more brittle, blocky, and jointed upsection. Locations of inventory squares

for imaging were determined by evaluating visible changes in lithological variation including

changes in relative bed thickness and brittleness of the shale beds within each outcrop.

Figure 14. Inventory Squares marked at different stratigraphic levels above the more ductile Chattanooga Shale. High Resolution LIDAR images for each inventory square are illustrated with (1) being along the base of the highly deformed ductile shale and (2) at a stratigraphically higher level.

27

At the FPO inventory squares were imaged at four stratigraphically distinct areas (Figure 15).

Two of the squares within the more deformed shale, near the basal contact with the Red

Mountain Formation, were of limited utility as fractures were sparse and irregular (Figure 16).

Further upsection the outcrop becomes notably brittle in nature and representative inventory

squares provide measurable well-developed natural fractures. Figure 17 provides the location

of the inventory squares, as well as the LIDAR images of the well jointed shale at each outcrop.

Figure 15. LIDAR images of Chattanooga Shale, FPO. From Inventory Squares across the formation. Squares (1) and (2) fall within the more ductile base and are of limited utility. Inventory Square (4) is a high resolution image of the more blocky, well jointed upper section.

28

Figure 16. LIDAR images of Chattanooga Shale within the highly deformed ductile section from the near the base of the formation along the contact with the Red Mountain Formation (1) and further upsection (2).

29

Figure 17. High Resolution LIDAR images from the brittle lithology at the BRO and the FPO.

30

Correlations between bed thickness and joint spacing yield indexes of fracture spacing (Fracture

Spacing Index, FSI) which are representative of localized strata and offer potential insight into

vertical and lateral variability. Appendix D includes data extracted from the LIDAR images and

processed data used to determine the FSI. The LIDAR images, which provide 3-D spatially

resolved data, facilitate point-to-point measurements of distance/separation enabling

determination of bed thickness and fracture spacing. For the Chattanooga Shale in this study,

FSI variation ranges from 1.09 to 1.53 and these values are essentially determined from the slope

of the plots of bed thickness to joint spacing. FSI measurements within each outcrop

demonstrate differences in fracture frequency suggesting lithologic variation and bedding

thickness exert a strong control.

Thin section analysis from four areas across the shale from the FPO provides opportunity for

insight into variability as changes in grain size and/or quartz content often correspond to

changes in brittle response. Thin sections and SEM micrographs illustrate the variability (Figure

18). Transmitted light in the microscopic analysis is useful for samples from the highly

deformed base of the Chattanooga Shale, proximal to the Red Mountain Formation, as well as

at the top of the formation near the contact with the overlying Maury Shale (18a and 18c).

However, with high concentrations of organics within the middle part of the shale section, using

transmitted light in microscopic imaging was limited necessitating the use of reflective light in

the petrographic work (Figure 18b) as well as wavelength dispersive backscattered SEM (18d)

on polished slides. The shale at the base (18a) of the formation, having experienced intense

deformation, appears to have large quartz grains that may represent alteration and growth

from greater temperature and pressures experienced along decollement horizons. The shale at

31

near the top of the formation (18c) might best be described as sandy shale with particles

commonly greater than 63 microns. Although reflective light microscopy of the black shale

within the formation (18b) provides a general image of the fabric, detail such as pyrite framboid

sizes and distributions (4 – 12 microns), are much more accessible by SEM (18d). Given that the

pyrite framboids range in diameter from those under 5 microns to those on the order of 10

microns, suggestive of mixed oxidation levels, the organic rich shale in of the Chattanooga Shale

appears to have been deposited in an environment in which oxygen richness varied over time.

FORT PAYNE OUTCROP (FPO) – FRACTURE ANALYSIS

At the FPO, the Chattanooga Shale is bounded at the base by the Silurian Red Mountain

Formation and above by a thin bed of Mississippian Maury Shale which is overlain by the

Mississippian Fort Payne Chert. The LIDAR image at the top of Figure 15, across the FPO was

made by imaging quadrants at several Scan-Positions (specific vantage points at which the

LIDAR images are obtained). Where stratigraphic levels within the outcrop were notably

distinct with respect to bed thickness as well as fracturing, inventory squares were marked for

imaging and analysis. Four Inventory Squares (1 m2) were marked along the FPO of

Chattanooga Shale. The upper two inventory squares, within the brittle Gassaway member,

yielded viable data for fracture analysis. See Appendix D for Fracture Data from LIDAR images

of Chattanooga Shale at BRO and FPO.

32

Figure 18. Polished thin sections from FPO Chattanooga Shale. a.) Large Quartz Grains near base of the Chattanooga Shale in the FPO using a Transmitted Light Petrographic Microscope. b.) Organic Rich Shaly Fabric using Reflected Light Petrographic Microscope. c.) Sandy Shale near top of the formation using Transmitted Light. d.) SEM image of Chattanooga Shale approximately 1 meter above the Red Mountain Sandstone with notable pyrite framboids.

33

Measurements of bed thickness and fracture separation, often seen as systematically spaced

jointing within the rock, were made manually on the Inventory Square images. With the use of

a workstation, the colored image and point cloud of spatially resolved data from LIDAR imaging,

facilitated the measurement of bed thickness and fracture separation. Plots of thickness

relative to spacing were generated from measurements within the LIDAR image by evaluating

the distance between selected points. Bed thickness measurements are tabulated and within a

given bed the spacing between fractures is cumulated and averaged. Fracture Density (or

Intensity) is similarly calculated by assessing the number of fractures per given length. Plots of

Fracture Density, in fractures per centimeter, relative to bed thickness are generated. Figure 19

presents plots of thickness relative to spacing and the density relationships to bed thickness

from data extracted from Inventory Square #4. In the plot of spacing to thickness the R2 value

at 0.73 suggests deviation from the expected linear correlation. For a given homogeneous

lithology, spacing is expected to be uniform with a uniform application of stress. Deviations

from linearity, as mentioned earlier, are worth noting as they suggest the system is more

complex, e.g., fractured beds might exhibit more than one fracture. Similarly the curve-fit of

fracture density relationship to thickness is not ideal with an R2 of 0.77.

Figure 20 illustrates similar data extracted from Inventory Square #3. The R2 values from the

graphs are reasonable, but a larger set of data might provide a more holistic picture of the shale

at this stratigraphic level. A FSI of 1.473 is seen at FPO within Inventory Square #3. A FSI of

1.086 is recorded at FPO within Inventory Square #4, though the measure of linearity, R2 , is

0.73.

34

Figure 19. (a) Plots of Spacing to Bed Thickness and (b) Density to Bed Thickness. FPO Inventory Square #4. The slope provides a Fracture Spacing Index of 1.086.

35

BIG RIDGE OUTCROP – FRACTURE ANALYSIS

At the BRO (Figure 14) two Inventory Squares

were imaged in the more brittle lithology (the

Gassaway member) above the ductile shear

zone. Plots of Fracture Spacing to Bed

Thickness exhibit linear relationships with high

R2 values, (Figure 21). Of note between the

two inventory squares, however, is the change

in the value of the slope of the lineations.

Slope in plots of spacing to bed thickness

essentially represents and index for fractures

where facies changes are occurring. The upper

Inventory Square has a distinctly higher slope suggestive of a lithologic change considering both

data sets are consistently linear in nature. The higher slope corresponds to a greater spacing

Figure 21. Plots of Fracture Spacing to Bed Thickness. BRO Inventory Squares #1 and #2. FSI value of 1.17 and 1.53, respectively

Figure 20. Plots of Spacing to Bed Thickness and Density to Bed Thickness. FPO Inventory Square #3. The slope provides a Fracture Spacing Index of 1.473.

36

between fractures for a given bed thickness. Lu (2015) records an increase in oxicity in tandem

with quartz influx as deposition continued throughout the Late Devonian, the change within the

depositional environment leading to more quartz rich beds of Chattanooga Shale correlates

with the observed brittle tendency and fracture response.

LIDAR APPLICATION RANGE LIMITS

With respect to LIDAR application, a ranging study was done to evaluate what data collection

parameters were viable to resolve bedding and fractures on the order of centimeters. The use

of LIDAR to evaluate features on the order of centimeters necessitated establishing actualistic

boundaries. The RIEGL VZ-400 has a defined step width of 7 cm at 100 meters distance with a

setting of 0.04⁰ angular resolution (RIEGL, 2015), meaning that a measured surface point has a

nearest neighbor at that distance. In practicality, for mapping and measuring surfaces of shale

for bed thickness and joint spacing the point-to-point separation must be at least an order of

magnitude lower than the distances being measured. Application of the RIEGl VZ-400

Terrestrial Laser System for imaging Chattanooga Shale beds on the order of centimeters,

required that angular resolution relationships be taken into consideration.

Images of a well-jointed face were done with incrementally increased step-widths (Figure 22),

and fixed distance from the outcrop. Figure 22a is an image made at 0.06⁰ resolution. Note the

meter stick scale bar along the wall at the bottom left. The image is greatly enhanced at 0.01⁰

resolution and the window for visual evaluation is improved. In terms of analysis this means

that one can pan in on the image up to the point of degraded visual quality. Two aspects of

collecting highly resolved images are important in planning the LIDAR survey. The areal extent

37

of the surface being imaged and the desired resolution are intrinsically tied together. Colorized

LIDAR images are created upon the conclusion of the scan as photographs from the attached

camera are used to colorize the laser scanned data. The photorealistic LIDAR images which

provide access to point-to-point distance measurements used for fracture spacing evaluation

impact the manual collection of spatially relevant data from outcrop features. The image in

Figure 22c was made at 0.007⁰ step-width resolution. Note that as the resolution changes,

although the visual details of the outcrop are improved, the window of visualization decreases.

The ability to measure smaller separation distances is improved at the cost of area being

imaged, with constraints on data management, time for data acquisition, as well as the demand

for advanced photographic means. The rich details which are provided within the point cloud of

data from close-proximity LIDAR scanning of fractured surfaces comes at the expense of system

data management as the amount of data the LIDAR system can actively buffer limits the scope of

the project. The system will stop scanning the surface if a large amount of data from close-up

imaging exceeds the data management capacity for the device. Field results of LIDAR imaging

suggest that attempting to acquire a highly resolved image (centimeter resolution) across a

broad surface (> 3 m2) will result in cessation of the laser scanning during data collection.

Acquisition time is of concern if large surface areas are being imaged. A 1 m2 inventory square

of a surface imaged at centimeter resolution takes approximately one hour once the system has

been set up, with larger surfaces more significant time demands will result. Consideration of

the photographic equipment used to colorize the LIDAR scans is necessary when taking close up

images as the colorization requires the digital photographs taken by the camera during the

project scanning to be of substantially high resolution (centimeters). Successful project

38

planning for LIDAR surveying of close-up surfaces involves consideration of these aspects as the

area to be studied is intrinsically tied to image resolution.

If very fine, detailed resolution is desired,

the window for imaging needs to be

correspondingly decreased to mitigate

workstation processor demands as well as

optical limitation from the attached camera

which colorizes the LIDAR images. The

present study involved step widths ranging

from 0.003 – 0.005⁰ resolution,

approaching the lower limit of the

instrument, with area scan windows set to

best represent the inventory squares and a

minimal area of adjacent rock.

Figure 22. Images of FPO Chattanooga Shale made at fixed distance with variable step-width for resolution evaluation. a.) larger window - low resolution image (0.06⁰), b.) moderate window – moderate resolution (0.01⁰), c.) small window – higher resolution (0.007⁰).

39

CHAPTER V

CONCLUSION

This study has shown that LIDAR imaging of outcrops can be effectively applied towards

determining the positive correlation between fracture spacing and fracture intensity variation

with bedding thickness in brittle shale units. LIDAR images obtained from two Chattanooga Shale

outcrops along the backlimb of the Wills Valley Anticline were evaluated for fracture intensity

variations within the brittle shale lithology. The two outcrops (Figure 9) were named BRO (Big

Ridge Outcrop) and FPO (Fort Payne Outcrop). At the BRO, a linear correlation between bed

thickness and fracture spacing was clearly established based on the statistical analysis of high

resolution LIDAR image data. The Fracture Spacing Index (FSI) notably varied stratigraphically

within the outcrop. The lower inventory square (Figure 17) yielded a FSI value of 1.17.

With increasing quartz content and bedding thickness upsection the FSI increased to 1.53

within the upper part of the formation (the Gassaway member). The Fracture Spacing Index

(FSI), a function of fracture intensity, is determined by measuring the slope of the Bed Thickness-

Fracture Spacing plot (Figure 21). Variation from 1.17 to 1.53 suggests that the shale fracturing is

not homogeneous within the Chattanooga. A first order observation would indicate that

mechanically distinct stratigraphic intervals exist within the BRO, which is not surprising as the

lithology, especially the quartz content (Figure 23), has been shown to vary upsection (Lu,

2015).

40

Plots of Thickness to Spacing for the BRO and

FPO have been constructed. The Plots of

Thickness to Spacing for BRO were

consistently linear with high R2 values (>

0.98). Plots of Thickness to Spacing for FPO

were of limited linearity for measurements

made within the brittle lithology inventory

squares in Fort Payne Outcrop with the

stratigraphically higher (Gassaway Member) inventory square section having an R2 of 0.77 and

the stratigraphically lower inventory square (the Dowelltown member) section having a slightly

better value at an R2 of 0.917.

Two aspects of this data can be addressed. The first is the limited utility of the FSI when the

plots are not linear and the second is the utility of LIDAR digitized data. Minimally, the limited

FSI suggests that within the outcrop at different stratigraphic levels and between the outcrops

along the backlimb there exists dissimilarity in fracture response to stress. Correlating

stratigraphic levels across the backlimb would be difficult to impossible given the tectonic

thickening (Coleman, et al., 1988) which may be present in the atypically thick outcrops of

Chattanooga Shale.

Noting the low R2 in the plot of Bed Thickness to Fracture spacing in Figure 24a, even more

evident in the plot of Fracture Density to Bed Thickness (24b), suggests that outliers exist within

the expected correlation. If the data point at 7 cm bed thickness with a fracture density of 0.2

F/cm. were removed from the data an improved R2 of 0.9296 results (24c). The goal is not to

Figure 23. Quartz to Clay Ratios determined by X-Ray Diffraction. FPO (Outcrop 1) and BRO (Outcrop 2), from Lu (2015).

41

dismiss outliers from statistical analysis but

to consider that re- evaluation of the digital

data might provide evidence that the

outlier does not belong in the fracture set

(i.e., a second fracture set or non-natural

fractures might exist). The data from the

fracture spacing/density analysis from

LIDAR images suggests that fracture density

variation exists both within each outcrop at

varying stratigraphic levels and along the

backlimb. Given the proximity of the

inventory squares within each formation,

the variation in fracture intensity is

derivative of lithologic variation.

Figure 24. Inventory Square #4, FPO Chattanooga Shale outcrop. a.) low R2 value (0.7304) evidences non- linearity, b.) Outliers are notable in curve, c.) Improvement in linearity, R2 of 0.9296.

42

CONCLUDING REMARKS

Literature reviews of the current state of LIDAR imaging together with the images obtained

during this study suggest that carefully planned survey programs will deliver optimal results.

Highly accurate, highly resolved images of surfaces can be made with the laser scanner, but as

the data volume grows workstation processing demands can easily become unmanageable.

Software developments in academic and industrial groups with focus on application to

geosciences are facilitating point cloud data processing, image interpretation, and incorporation

into models (Rarity, et al., 2014). Workflow in outcrop selection, choice in scanning locations,

and choice of resolution specific to the representative feature(s) still remains the key element in

LIDAR surveys. User-oversight in data processing and interpretation is essential to

incorporation of the spatially resolved data into 3D models.

During this study, the opportunity to collect high resolution digital images of inventory squares

at outcrops of Chattanooga Shale along the backlimb of the Wills Valley Anticline served to

prove the concept of application of LIDAR imaging for determining fracture density

relationships. In shale units, provenance studies would be of interest to potentially tie

stratigraphic variation and lithologic variation associated with changes in brittleness.

The development of a cellular model from the laser scanned images would improve the

accuracy of models with more realistic representation of fracturing as well as an improvement

on fluid flow parameters. With the LIDAR imaging one can expect to collect highly accurate,

representative attributes, such as fracture spacing or intensity that are useful for incorporation

into reservoir models for estimating the flow of fluids during the production.

43

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APPENDIX A

RIEGL LASER MEASUREMENT SYSTEMS

RIEGL Laser Measurement Systems GmbH, headquartered in Austria, is recognized locally as

RIEGL USA an industrial leader in performance of their 3D Laser Scanners offering variety in

Terrestrial LIDAR Systems. The LIDAR system used in this work is the VZ-400 with a functional

range roughly between 2 to 500 meters and accuracy with respect to laser imaging of roughly

Differences in LIDAR laser scanners. Data from currently available systems from RIEGL. (RIEGL, 2015)

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0.5 cm. Images acquired from this system, for this study, were typically done between 5 to 15

meters from the outcrop surface. LIDAR imaging systems operate on a speed of light, typically

an infrared laser, in combination with travel time from the fixed LIDAR system to a remote

surface and back allowing for distance determination. As the laser scans, both vertically and

horizontally across a surface, 3-D spatialization is realized. The resolution of the 3-Dimensional

spatial relationships is derived from the step-widths both horizontally and vertically as the laser

steps across the surface, and is subject to user-control with a limit of 0.0024⁰ for the VZ-400.

The high resolution images can be further enhanced with corresponding camera images

imparting essentially true color to the spatial data-set.

The Principles of Operation illustration maps out the essential elements of a typical terrestrial

LIDAR imaging system as produced by RIEGL. The cylindrical operating unit has local control

through a keypad (element 4) although a field laptop (element 10) equipped with the LIDAR

system facilitates many aspects of data collection. The key operational components are the

optical head (element 3) allowing 360⁰ horizontal scanning, the mirror (element 2) which

provides for vertical scanning, and the image calibrated camera (element 9) for high resolution

coloration of the laser generated spatially resolved images.

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Principle of Operation Detail. Elements of a typical terrestrial laser scanner are outlined. The pictured model is of the RIEGL VZ-400 high accuracy LIDAR scanner. This illustration is used with permission from RIEGL Laser Measurement Systems. (RIEGL, 2015)

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The laser scanning units are portable facilitating field use and imaging spatially complex

features from multiple perspectives. Multiple scan-positions can be used to image complex

geologic outcrop features which can be combined into a 3-D view within RIEGL’s proprietary

RiSCAN PRO software. Within the software the data rich point-cloud can be used to measure

point to point distances or derivatives e.g. area, perimeter. Some current technological

limitations are from issues resulting from demands on computer processing abilities. Large

data-sets from high resolution images lead to heavy demands on power, memory, and

processing ability.

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APPENDIX B

LIDAR IMAGING OF OUTCROPS – WORKFLOW

Preparation for use of the LIDAR imaging system involves placement of the system, including

the mounted camera, laptop, and power source, at an appropriate distance and orientation to

the outcrop and identification of other scan-positions to ensure full coverage of surface being

imaged. Placement of reflector targets is done for internal control (tie points) for registration

from multiple scan-positions.

Use of additional Scan Positions involves repeating steps 4 – 9.

Primary Scan – Steps in Data Acquisition

(1) Create New Project within RiSCAN Pro.

(2) Camera Setup and Configuration.

(3) Set Reflector Type

(4) Establish New Scan Position, Establish New Single Scan.

(5) Setup Panorama view for 360⁰ imaging, set desired resolution via adjustment of step

widths along both the horizontal and vertical.

(6) Initiate LIDAR Scanning along with Image Acquisition.

(7) Identify and Mark reflective tie points.

(8) Finescan tie points.

(9) Registration/Find Corresponding Points.

(10) Additional High Resolution Area of Interest Scan from same Scan Position is done by

establishing a New Single Scan within a defined window adjusting step widths accordingly.

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APPENDIX C

LIDAR BASED FRACTURE STUDIES

LIDAR is being used more frequently in outcrop studies because high-resolution surface

representations can be used for geological constraints in reservoir modelling (Rarity, et al.,

2014; Ahlgren and Holmlund, 2003; Buckley, et al., 2010; Hodgetts, 2013; Bellian, et al., 2005;

Monsen, et al.,2006; Ahmadzamri, et al., 2014). Digital outcrop models, or virtual outcrops,

developed from laser scanned images provide volumes of spatially resolved data which reduce

model uncertainty and improve the reservoir characterization (Adams, et al., 2009;

Ahmadzamri, et al., 2014; Minisini, et al., 2014) especially following a guided workflow (Enge, et

al., 2008). The workflow typically consist of outcrop selection, data collection, and geological

interpretation. Followed by the eventual construction and testing of a data-constrained

geocellular model (Enge, et al., 2007). In the model, variability in reservoir zones can be

accounted for by smaller area ‘cells’ representing significant differences within the rock.

LIDAR imaging of outcrops provides many benefits, including surface area coverage which

cannot be fully acquired by manual techniques (Monsen, et al., 2006). Moreover, digitized data

can impart realistic geological heterogeneity into reservoir characterization models (Monsen, et

al., 2011, Olariu, et al., 2008; Kurtzman, et al., 2009) and provide higher resolution of fracture

details below than can be provided by conventional seismic techniques (Wilson, et al, 2011). In

addition, acquisition of digital outcrop data allows automation in data processing and analysis

(Asssali, et al., 2014; Monsen, et al., 2006; Seers and Hodgetts, 2014; Monsen, et al., 2011).

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Several studies have applied LIDAR in tandem with modelling techniques to characterize

fractures: They focused on determining the relationship between a) orientation and spacing of

fractures b) stratigraphy and lithologic variability in layers; and c) fold curvature and natural

fractures (Pearce, et al., 2011; Olariu, et al., 2008; Zahm, et al., 2010; Wilson, et al., 2011). Each

of these studies contribute insight into the application of the LIDAR surveys in fracture studies

and reservoir models.

In recent years, there have been many studies using LIDAR images to study natural fractures

for outcrops scale reservoir characterization. Olariu, Zahm, and Wilson have done fracture

evaluation studies using LIDAR, over the past decade, providing much of the impetus for this

body of research. Olariu, et al., 2008, characterized deep-water Pennsylvanian Jackfork

sandstones by terrestrial laser scanned images of outcrops within the Big Rock Quarry, Arkansas.

They focused on the application of an algorithm to extract surface orientations from point cloud

data. The applied algorithm functions by pattern recognition done through successive analysis

of clusters. They found that fracture planes from thicker beds with larger fracture spacing were

easier to identify with larger numbers of data-points defining the surface, notably emphasizing

that orientation was more difficult to recognize with thinner beds.

Zahm, et al., 2010, evaluated fracture distributions resulting from stratigraphic architecture,

and degree of faulting, within carbonate damage zone in the Balcones Fault Zone in central

Texas by LIDAR survey. They determined that deformation was more intense in facies rich in

mud, with thinner beds, which leads to reduction in rock strength. The LIDAR survey allowed

Zahm, et al. (2010) to make surface measurements of strike and dip for faults, shear fractures,

and joints within the outcrop. Fracture intensity was measured using a 0.5m x 0.5m grid

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overlying the outcrop photo model. They determined fracture intensity within the grid by the

total trace length of fractures within the grid relative to the grid area. Grids in non-faulted

sections yielded deformation intensities roughly between 1 to 2 m/m2. In grids within faulted

sections the distribution fell between 1 to 3 m/m2. The thicker bedded, grain dominated, more

competent facies tended to have less deformation as measured by deformation intensity

squares.

Wilson, et al., 2011, used LIDAR surveying images of Austin Chalk fracture networks to extract

spatial relationships enabling the construction of discrete fracture network (DFN) models.

Manual and semi-automated methods were applied to frequencies and orientations of

fractures sets with variable results. The fracture data, derived from LIDAR images, was used to

construct a DFN model allowing flow simulations.

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APPENDIX D

FRACTURE DATA FROM LIDAR IMAGES AT CHATTANOOGA SHALE OUTCROPS

Bed Thickness and Fracture Spacing measurements in pre-marked Inventory Squares have been

made within the RiSCAN Pro software as the imaged surface is a point cloud constituted with

positional information. Picking two points within the image enabled the determination of

distance or separation. Measurements of Bed Thickness and Fracture Spacing within the given

bed were made and recorded.

In general Bed Thickness varied within the Inventory Squares (#3 and #4) of Chattanooga Shale

found in Fort Payne from roughly 1 to 10 centimeters though most commonly around 2

centimeters. The average thickness was 4.2 centimeters. The Bed Thickness varied within

Inventory Squares (#1 and #2) of Chattanooga Shale found along the Big Ridge road cut from

roughly 2 to 14 centimeters though most commonly around 7 centimeters. The average

thickness was 5.5 centimeters. The following tables were derived from data collected from

images made at each outcrop.

Fracture Spacing Index values range from 1.09 to 1.53 and are determined by determining the

linear equation fitting plots of bed thickness to fracture spacing and solving for the slope.

These values were determined for each inventory square and are descriptive at the respective

stratigraphic intervals.

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Inventories of Fractures within the Chattanooga Shale in Fort Payne, Alabama.

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Inventories of Fractures within the Chattanooga Shale along the Big Ridge Road Cut (I-59), Alabama.